CONF pinto:icassp-phnrecog:2008/IDIAP Exploiting Contextual Information for Improved Phoneme Recognition Pinto, Joel Praveen Hermansky, Hynek Yegnanarayana, B. Magimai.-Doss, Mathew EXTERNAL http://publications.idiap.ch/attachments/papers/2008/pinto-icassp-phnrecog-2008.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/pinto:rr07-65 Related documents "IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP)" 2008 IDIAP-RR 07-65 In this paper, we investigate the significance of contextual information in a phoneme recognition system using the hidden Markov model - artificial neural network paradigm. Contextual information is probed at the feature level as well as at the output of the multilayerd perceptron. At the feature level, we analyse and compare different methods to model sub-phonemic classes. To exploit the contextual information at the output of the multilayered perceptron, we propose the hierarchical estimation of phoneme posterior probabilities. The best phoneme (excluding silence) recognition accuracy of 73.4\% on the TIMIT database is comparable to that of the state-of-the-art systems, but more emphasis is on analysis of the contextual information. REPORT pinto:rr07-65/IDIAP Exploiting Contextual Information for Improved Phoneme Recognition Pinto, Joel Praveen Yegnanarayana, B. Hermansky, Hynek Magimai.-Doss, Mathew EXTERNAL http://publications.idiap.ch/attachments/reports/2007/pinto-idiap-rr-07-65.pdf PUBLIC Idiap-RR-65-2007 2007 IDIAP In this paper, we investigate the significance of contextual information in a phoneme recognition system using the hidden Markov model - artificial neural network paradigm. Contextual information is probed at the feature level as well as at the output of the multilayerd perceptron. At the feature level, we analyse and compare different methods to model sub-phonemic classes. To exploit the contextual information at the output of the multilayered perceptron, we propose the hierarchical estimation of phoneme posterior probabilities. The best phoneme (excluding silence) recognition accuracy of 73.4\% on the TIMIT database is comparable to that of the state-of-the-art systems, but more emphasis is on analysis of the contextual information.